1.1 Viz Top 5K Gene Dataset

In [44]:
import pandas as pd
import gene_exp_10x

import matplotlib.pyplot as plt
%matplotlib inline 
from copy import deepcopy
In [5]:
from clustergrammer2 import net
In [6]:
def umi_norm(df):
    # umi norm
    barcode_umi_sum = df.sum()
    df_umi = df.div(barcode_umi_sum)
    return df_umi
In [14]:
df_meta = pd.read_csv('../download/atlas/meta.tab', sep='\t', index_col=0)
df_meta.shape

/Users/nickfernandez/anaconda3/envs/py36lab/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (27) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
Out[14]:
(139331, 27)
In [8]:
df_ini = gene_exp_10x.load_gene_exp_to_df('../download/atlas/atlas_data_5k-sum/')
df_ini.shape
Out[8]:
(5000, 139331)
In [10]:
df_ini.columns.tolist()[:3]
Out[10]:
['cell_1', 'cell_2', 'cell_3']
In [15]:
df_meta.columns.tolist()
Out[15]:
['barcode',
 'sample',
 'stage',
 'sequencing.batch',
 'theiler',
 'doub.density',
 'doublet',
 'cluster',
 'cluster.sub',
 'cluster.stage',
 'cluster.theiler',
 'stripped',
 'celltype',
 'colour',
 'umapX',
 'umapY',
 'haem_gephiX',
 'haem_gephiY',
 'haem_subclust',
 'endo_gephiX',
 'endo_gephiY',
 'endo_trajectoryName',
 'endo_trajectoryDPT',
 'endo_gutX',
 'endo_gutY',
 'endo_gutDPT',
 'endo_gutCluster']

Drop doublets

In [20]:
ser_doublet = df_meta['doublet']
ser_singlet = ser_doublet[ser_doublet == False]
print(ser_doublet.shape)
print(ser_singlet.shape)

(139331,)
(125775,)
In [21]:
ser_ct = df_meta['celltype']
In [26]:
ser_ct.value_counts().sort_values(ascending=False)
Out[26]:
Epiblast                          14619
ExE ectoderm                      11758
ExE endoderm                       9339
Primitive Streak                   7265
Rostral neurectoderm               5392
Nascent mesoderm                   5028
Mesenchyme                         4979
Forebrain/Midbrain/Hindbrain       4854
Paraxial mesoderm                  3811
Intermediate mesoderm              3551
Surface ectoderm                   3523
Pharyngeal mesoderm                3086
Erythroid1                         2929
Haematoendothelial progenitors     2733
Erythroid3                         2697
Blood progenitors 2                2587
ExE mesoderm                       2571
Caudal epiblast                    2445
Somitic mesoderm                   2079
NMP                                2041
Mixed mesoderm                     1994
Gut                                1940
Allantois                          1820
Spinal cord                        1796
Visceral endoderm                  1283
Cardiomyocytes                     1206
Anterior Primitive Streak          1158
Def. endoderm                      1108
Erythroid2                         1106
Endothelium                        1084
Caudal Mesoderm                    1075
Caudal neurectoderm                 948
Blood progenitors 1                 745
Neural crest                        622
Notochord                           464
PGC                                 392
Parietal endoderm                   284
Name: celltype, dtype: int64
In [25]:
ser_ct.value_counts().sort_values(ascending=False).plot('bar', figsize=(15,5))
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x1200b3320>

Add Cell Type Category

In [33]:
ser_ct['cell_1']
Out[33]:
'Epiblast'
In [35]:
ser_ct[cols[0]]
Out[35]:
'Epiblast'
In [42]:
cols = df_ini.columns.tolist()
new_cols = [(x, 'Cell Type: ' + str(ser_ct[x])) for x in cols]
In [45]:
print(new_cols[0])
df_cat = deepcopy(df_ini)
df_cat.columns = new_cols

('cell_1', 'Cell Type: Epiblast')
In [46]:
df_cat.columns.tolist()[:5]
Out[46]:
[('cell_1', 'Cell Type: Epiblast'),
 ('cell_2', 'Cell Type: Primitive Streak'),
 ('cell_3', 'Cell Type: nan'),
 ('cell_4', 'Cell Type: nan'),
 ('cell_5', 'Cell Type: ExE ectoderm')]
In [47]:
gene_sum = df_cat.sum(axis=1)
gene_sum.shape
Out[47]:
(5000,)
In [48]:
num_cells = 10000
df_small = umi_norm(df_cat.iloc[:,:num_cells])
df_small.shape
Out[48]:
(5000, 10000)
In [49]:
net.load_df(df_small)
net.filter_N_top(inst_rc='row', N_top=250, rank_type='var')
net.normalize(axis='row', norm_type='zscore')
net.clip(-5,5)
net.load_df(net.export_df().round(2))
net.widget()
In [ ]: